The State of AI in Healthcare

There were two main take-aways from the conference.

First, machine learning & AI applications in Healthcare abound in areas as varied as genealogy, drug discovery, the diagnosis of breast cancer, of diabetic retinopathy, as well as in developing responses to the opioid epidemic (an area of our work).

Or specific note is that while AI diagnostic approaches do tend to outperform human pathologists, AI approaches in combination with human pathologists produce the best results. So, AI may not lead to job losses in healthcare, but may aid physicians in improving the overall level of care available to patients.

Essentially, the AI may provide a “Tap on the Shoulder” that alerts physicians on what to look for.

Second – the risk of blindly following algorithms is great. In short – death.

Training AI applications requires “clean” data – that is, data that defines patient characteristics, diagnosis, and outcomes. Often such data sets are incomplete, inaccurate, or both. In addition, the data sets that are available are, naturally, infused with all our own human cognitive biases and prejudices.

Diagnostic and treatment algorithms trained with bad data shouldn’t be expected to immediately replace trained & experienced clinicians, improve outcomes, or decrease system costs. These algorithms will experience more growing pains in their development and deployment than has been advertised. But that should be expected – the practice of medicine is complicated, why wouldn’t the training and deployment of machine learning and AI in healthcare be equally complicated?

So, What About the Doctor?

In Star Trek Voyager, the ship’s doctor and nurse passed away in the first episode and for the rest of the series the Emergency Medical Hologram Mark I provided all medical care. The advances in diagnostic capabilities suggest that The Doctor, as the Hologram came to be known, is certainly the direction we are headed – but much remains to be done before a computer program can visual inspect, diagnose, and prescribe a treatment for a patient in an unassisted manner. That, however, is the future into which we Boldly Go.

What’s Next For Us

Maintaining the currency of the database supporting the GeoHealth Dashboard is an ongoing effort. We also continue to talk publically about our approach to geospatial data analytics for population health and specifically to address the opioid epidemic. Our next talk is at the 4th Annual Advanced Healthcare Analytics Summit – I take my turn behind the podium and present our work towards developing effective responses to the opioid epidemic. In September, I Chair the Artificial Intelligence Innovations Summit in San Francisco.

And, of course, we are exploring every opportunity to put our solution into practice.